This article presents a survey of reinforcement learning algorithms for Markov Decision Processes (MDP). In the first half of the article, the problem of value estimation is considered. Here we start by describing the idea of bootstrapping and temporal difference learning. Next, we compare incremental and batch algorithmic variants and discuss the impact of the choice of the function approximat...

In this paper, we harness the synergy between two important learning paradigms, namely, active learning and domain adaptation. We show how active learning in a target domain can leverage information from a different but related source domain. Our proposed framework, Active Learning Domain Adapted (Alda), uses source domain knowledge to transfer information that facilitates active learning in th...

the need for intercultural awareness and skills emerges strongly in both distance learning courses, and in social life in multicultural societies. the study of online language transactions is therefore an important aspect of the emerging culture and sociolinguistics of computer mediated communication. the research reported in this paper concerns perceptions held by students in an iranian univer...

In this paper we propose a new algorithm for learning polyhedral classifiers which we call as Polyceptron. It is a Perception like algorithm which updates the parameters only when the current classifier misclassifies any training data. We give both batch and online version of Polyceptron algorithm. Finally we give experimental results to show the effectiveness of our approach.

Instrumental variable regression (IVR) is a statistical technique utilized for recovering unbiased estimators when there are errors in the independent variables. Estimator bias in learned time series models can yield poor performance in applications such as long-term prediction and filtering where the recursive use of the model results in the accumulation of propagated error. However, prior wor...

Traditional supervised learning is formulated as learning from a given data set while being able to generalize to unseen data. It is usually assumed that both the given and unseen data are drawn iid from the same (unknown) distribution. In online learning, we make no assumption about the source of data. One simply observes a stream of data coming from some arbitrary source one by one. At every ...

We have recently been studying the case where have a training set T generated from an underlying distribution and our goal is to find some good hypothesis, with respect to the true underlying distribution, using the training set T . We now examine how to use online learning algorithms (which work on individual, arbitrary sequences) in a stochastic setting. Let us consider the training set T as ...

Learning for maximizing AUC performance is an important research problem in Machine Learning and Artificial Intelligence. Unlike traditional batch learning methods for maximizing AUC which often suffer from poor scalability, recent years have witnessed some emerging studies that attempt to maximize AUC by single-pass online learning approaches. Despite their encouraging results reported, the ex...

Malicious Web sites are a cornerstone of Internet criminal activities. The dangers of these sites have created a demand for safeguards that protect end-users from visiting them. This article explores how to detect malicious Web sites from the lexical and host-based features of their URLs. We show that this problem lends itself naturally to modern algorithms for online learning. Online algorithm...

Intrusion detection is an active research field in the development of reliable web-based information systems, where many artificial intelligence techniques are exploited to fit the specific application. Although some detection algorithms have been developed, they lack the adaptability to the frequently changing network environments, since they are mostly trained in batch mode. In this paper, we...